---
license: apache-2.0
language:
- en
- zh
library_name: gguf
pipeline_tag: image-text-to-text
base_model: meta-models/Muse-Glimmer-30B
base_model_relation: quantized
tags:
- gguf
- llama.cpp
- muse-glimmer
- abliterated
- uncensored
- crack
- reasoning
- tool-use
- atem
---

<p align="center">
  <img src="dealign_logo.png" alt="Dealign.ai" width="180"/>
  <br/><strong><a href="https://dealign.ai">Dealign.ai</a></strong>
</p>

# Muse-Glimmer-30B-CRACK-GGUF

**CRACK-abliterated Muse Glimmer 30B — GGUF quants for llama.cpp.** Three quantizations
(Q8_0 / Q4_K_M / Q2_K) in one repository. Refusal behavior removed while preserving the
model's knowledge, reasoning, multi-strength thinking, and ATEM tool-calling.

> Research artifact with reduced safety guardrails. Use responsibly and lawfully.

## Quantizations

| File | Size | Notes |
|---|---|---|
| `Q8_0` | 29.6 GB | near-lossless reference |
| `Q4_K_M` | 16.9 GB | balanced (recommended) |
| `Q2_K` | 10.7 GB | smallest |

Pick one text file **plus** the vision projector `mmproj-Muse-Glimmer-30B-f16.gguf` (3.8 GB)
for image input. `Q4_K_M` is the recommended balance; `Q8_0` is near-lossless; `Q2_K` is smallest.

## Benchmarks

Evaluated through llama.cpp at greedy decoding. **MMLU** is logit-mode accuracy (base vs.
CRACK at the same quant — measures knowledge retention). **HarmBench** is answer-channel
compliance on harm behaviors, counting only coherent responses.

| Quant | MMLU (base) | MMLU (CRACK) | ΔMMLU | HarmBench compliance |
|---|---|---|---|---|
| `Q8_0` | 80.0% | 79.0% | -1.05 pp | 99.6% |
| `Q4_K_M` | 80.0% | 78.6% | -1.40 pp | 100.0% |
| `Q2_K` | 77.5% | 77.9% | +0.35 pp | 99.6% |

MMLU is retained within noise of the base model at every quant. HarmBench compliance is
reported for the CRACK model.

### HarmBench compliance by topic (CRACK)

| Topic | Compliance |
|---|---|
| chemical biological | 100.0% |
| cybercrime intrusion | 100.0% |
| harassment bullying | 100.0% |
| harmful | 100.0% |
| illegal | 100.0% |
| misinformation disinformation | 100.0% |

## Usage (llama.cpp)

```bash
llama-cli -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf -cnv \
  --temp 1.0 --top-p 0.95 --top-k 64
# or serve:
llama-server -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf --jinja \
  --temp 1.0 --top-p 0.95 --top-k 64 -c 8192
```

**Recommended sampling (baked into the GGUF):** `temperature=1.0`, `top_p=0.95`,
`top_k=64`. Token IDs: BOS `200000`, EOS `200001`/`<|eot|>`, pad `200018`.

### Reasoning strength
Muse Glimmer supports controllable reasoning. Set it via the chat template:
```json
{"chat_template_kwargs": {"reasoning_strength": "low"}}   // low | medium | high | xhigh
```
The reasoning trace is emitted on a separate channel (`reasoning_content`); the final
answer is the assistant content.

### Tool calling (ATEM)
The model emits ATEM-format tool calls, parsed natively by llama.cpp's `--jinja` server
into standard `tool_calls`. Pass OpenAI-style `tools` to the chat endpoint.

## Vision (image + text)

This is a multimodal model. Download a text quant **and** `mmproj-Muse-Glimmer-30B-f16.gguf`:

```bash
llama-mtmd-cli -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-f16.gguf --jinja \
  --image photo.jpg -p "Describe this image."
# or serve with vision:
llama-server -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-f16.gguf --jinja -c 8192
```

The same `mmproj` works with all three text quants.

## License
Apache 2.0. The upstream Muse Glimmer Usage Policy applies.

## Contact
eric@dealign.ai